K-means clustering algorithm in kernel function space

被引：0
|
作者：
Liang, JZ [1 ]
Wang, JY [1 ]
Xu, XB [1 ]
机构：
[1] Zhejiang Normal Univ, Sch Informat Sci & Engn, Jinhua 321004, Peoples R China
来源：
PROGRESS IN INTELLIGENCE COMPUTATION & APPLICATIONS | 2005年
关键词：
K-means clustering; kernel functions; learning algorithm; linear separability; Corpus Clustering;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
K-Means algorithm hardly attains higher accuracy for sparsely distributed samples. While a nonlinear separable problem can be changed to a linear (or approximately linear) separable one, by using kernel function method which through mapping it into a kernel space. The authors in this paper propose and study the K-Means clustering algorithm in kernel function characteristic spaces, analyze the complexity of the algorithm and get a method to lower the complexity.
引用
收藏
页码：642 / 646
页数：5
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